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Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study
Lysbeth Floden1, Melanie L Bell2
1Mel and Enid College of Public Health, University of Arizona, 1295 N. Martin Ave, Tucson, AZ, 85724, USA. lford@email.arizona.edu.
BMC Medical Research Methodology
|July 27, 2019
Summary
Multiple imputation of continuous outcomes is superior to non-response imputation for handling missing data in clinical trials. Imputing the continuous variable before dichotomizing offers the least bias, especially with substantial missing data.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Longitudinal Data Analysis
Background:
- Continuous outcomes in clinical trials are often dichotomized for responder analysis.
- Handling missing data in responder analysis commonly involves imputing as non-responders, despite known biases.
- Multiple imputation is an alternative, but its application to dichotomized continuous outcomes requires careful consideration of imputation model specifications.
Purpose of the Study:
- To compare the performance of multiple imputation on continuous versus dichotomized outcomes.
- To evaluate the bias and accuracy of non-response imputation versus multiple imputation methods.
- To assess imputation strategies in longitudinal clinical trial data.
Main Methods:
- Simulated a two-arm randomized controlled trial with a continuous outcome across four time points.
- Introduced missing data using six missing at random mechanisms.
- Compared three imputation methods: non-response imputation, multiple imputation before dichotomization, and multiple imputation after dichotomization.
Main Results:
- Multiple imputation methods showed reduced bias and type 1 error compared to non-response imputation.
- When missing data exceeded 30%, imputing the continuous outcome before dichotomizing generally yielded lower bias.
- Non-response imputation produced biased estimates, under- or overestimating treatment effects, and underestimated the difference in proportions in trial data.
Conclusions:
- Multiple imputation of the continuous outcome prior to dichotomization is recommended, especially with higher missing data rates.
- Imputing the continuous variable demonstrated less bias and better confidence interval coverage than imputing the dichotomous response at high missingness.
- Multiple imputation utilizing longitudinally measured continuous outcomes generally outperforms imputing missing data as non-responders.
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